An Analysis of Concealed Object Detection Using Decision Tree and Random Forest Algorithms - Shintre, Pallavi; Mahapatra, Shweatlana; Vincent, Shweta; Prakash Kumar, Om (International Journal of Engineering and Technology, 2018)
This paper presents a comparative study of the Decision tree algorithm and Random Forest algorithm, both using Haar wavelet transform to classify a concealed object as a threat or not. This finds its applications in airports and railway stations where passenger security is a major concern. A sub-millimeter wave image of a person having a concealed weapon on his thigh has been treated as the test dataset. The Haar wavelet transform along with the aforementioned algorithms is applied on the image to classify the patches in the images as threat or no threat regions. It is found that the Random Forest algorithm outperforms the Decision tree algorithm in terms of accuracy of detection as well as number of false positive generation.Â Â Â